Case-control association testing by graphical modeling for the Genetic Analysis Workshop 17 mini-exome sequence data.
Case-control association testing by graphical modeling for the Genetic Analysis Workshop 17 mini-exome sequence data.
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DOI：
10.1186/1753-6561-5-s9-s62
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发表时间：
2011-11-29
期刊：
影响因子：
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通讯作者：
Thomas A
中科院分区：
文献类型：
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作者：
Abel HJ;Thomas A
我们概括了最近关于连锁不平衡图形模型的工作，以估计遗传分析研讨会 17 个不相关个体数据集中个体的所有变量之间的条件独立结构。使用逐步提高计算效率的方法和我们之前描述的方法的扩展，我们估计了一个模型，该模型描述了疾病特征、所有定量变量、所有协变量、种族起源以及与这些变量最密切相关的基因座之间的关系。我们对前 50 个重复数据集进行了分析。我们发现我们的方法能够描述结果和协变量之间的关系，并且它可以正确检测疾病与多个位点的关联，并具有合理的假阳性检测率。
We generalize recent work on graphical models for linkage disequilibrium to estimate the conditional independence structure between all variables for individuals in the Genetic Analysis Workshop 17 unrelated individuals data set. Using a stepwise approach for computational efficiency and an extension of our previously described methods, we estimate a model that describes the relationships between the disease trait, all quantitative variables, all covariates, ethnic origin, and the loci most strongly associated with these variables. We performed our analysis for the first 50 replicate data sets. We found that our approach was able to describe the relationships between the outcomes and covariates and that it could correctly detect associations of disease with several loci and with a reasonable false-positive detection rate.